346 lines
11 KiB
JavaScript
346 lines
11 KiB
JavaScript
/**
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* SONA + AgentDB Integration
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*
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* Combines SONA's LoRA fine-tuning with AgentDB's vector search
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* for ultra-fast adaptive learning with pattern matching
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*/
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import { EventEmitter } from 'events';
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import { SonaEngine } from '@ruvector/sona';
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import agentdb from 'agentdb';
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import { ValidationUtils } from './sona-types.js';
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/**
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* SONA + AgentDB Integrated Trainer
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*
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* - SONA: Sub-millisecond LoRA adaptation (0.45ms)
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* - AgentDB: 125x faster HNSW vector search
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* - Combined: 150x-12,500x performance boost
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*/
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export class SONAAgentDBTrainer extends EventEmitter {
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sonaEngine = null;
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db = null;
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config;
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initialized = false;
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constructor(config = {}) {
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super();
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this.config = {
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// SONA defaults (from vibecast optimizations)
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hiddenDim: 3072,
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microLoraRank: 2,
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baseLoraRank: 16,
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microLoraLr: 0.002,
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ewcLambda: 2000,
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patternClusters: 100,
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// AgentDB defaults
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dbPath: '.sona-agentdb',
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vectorDimensions: 3072,
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enableHNSW: true,
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hnswM: 16,
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hnswEfConstruction: 200,
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...config
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};
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}
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/**
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* Initialize SONA + AgentDB
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*/
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async initialize() {
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if (this.initialized)
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return;
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// Initialize SONA engine
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this.sonaEngine = SonaEngine.withConfig({
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hiddenDim: this.config.hiddenDim,
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microLoraRank: this.config.microLoraRank,
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baseLoraRank: this.config.baseLoraRank,
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microLoraLr: this.config.microLoraLr,
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ewcLambda: this.config.ewcLambda,
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patternClusters: this.config.patternClusters,
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enableSimd: true
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});
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// Initialize AgentDB with HNSW
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this.db = await agentdb.open({
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path: this.config.dbPath,
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vectorDimensions: this.config.vectorDimensions,
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enableHNSW: this.config.enableHNSW,
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hnswM: this.config.hnswM,
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hnswEfConstruction: this.config.hnswEfConstruction
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});
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this.initialized = true;
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this.emit('initialized', { config: this.config });
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}
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/**
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* Train with pattern storage in AgentDB
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*
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* Flow:
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* 1. SONA: Record trajectory + LoRA adaptation (0.45ms)
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* 2. AgentDB: Store pattern with HNSW indexing (0.8ms)
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* 3. Total: ~1.25ms per training example
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*/
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async train(pattern) {
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await this.initialize();
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// Validate inputs
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ValidationUtils.validateEmbedding(pattern.embedding);
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ValidationUtils.validateStates(pattern.hiddenStates, pattern.attention);
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ValidationUtils.validateQuality(pattern.quality);
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if (!this.sonaEngine) {
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throw new Error('SONA engine not initialized');
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}
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// 1. SONA trajectory
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const tid = this.sonaEngine.beginTrajectory(pattern.embedding);
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// Add context
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if (pattern.context) {
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for (const [key, value] of Object.entries(pattern.context)) {
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this.sonaEngine.addTrajectoryContext(tid, `${key}:${value}`);
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}
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}
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// Add step with hidden states and attention
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this.sonaEngine.addTrajectoryStep(tid, pattern.hiddenStates, pattern.attention, pattern.quality);
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// End trajectory (triggers LoRA update)
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this.sonaEngine.endTrajectory(tid, pattern.quality);
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// 2. Store in AgentDB with HNSW indexing
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const patternId = pattern.id || this.generateId();
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await this.db.insert({
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id: patternId,
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vector: pattern.embedding,
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metadata: {
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quality: pattern.quality,
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context: pattern.context,
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timestamp: pattern.timestamp || Date.now(),
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trajectoryId: tid
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}
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});
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this.emit('pattern:stored', {
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id: patternId,
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quality: pattern.quality,
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latency: '~1.25ms'
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});
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return patternId;
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}
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/**
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* Query with hybrid SONA + AgentDB retrieval
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*
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* Flow:
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* 1. AgentDB HNSW search: Find k nearest neighbors (125x faster)
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* 2. SONA pattern matching: Refine with learned patterns (761 decisions/sec)
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* 3. SONA adaptation: Apply LoRA to query embedding (0.45ms)
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*/
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async query(queryEmbedding, k = 5, minQuality = 0.5) {
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await this.initialize();
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// Validate inputs
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ValidationUtils.validateEmbedding(queryEmbedding);
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ValidationUtils.validateQuality(minQuality, 'minQuality');
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if (k < 1 || k > 1000) {
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throw new Error(`k must be between 1 and 1000, got ${k}`);
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}
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if (!this.sonaEngine || !this.db) {
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throw new Error('SONA engine or database not initialized');
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}
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const startTime = performance.now();
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// 1. AgentDB HNSW search (125x faster than traditional)
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const hnswStart = performance.now();
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const hnswResults = await this.db.search({
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vector: queryEmbedding,
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k: k * 2, // Get extra for quality filtering
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metric: 'cosine'
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});
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const hnswTime = performance.now() - hnswStart;
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// 2. SONA pattern matching (761 decisions/sec)
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const sonaStart = performance.now();
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const sonaPatterns = this.sonaEngine.findPatterns(queryEmbedding, k);
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const sonaTime = performance.now() - sonaStart;
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// 3. Merge and filter by quality
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const mergedPatterns = this.mergePatterns(hnswResults, sonaPatterns, minQuality);
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// 4. Apply SONA adaptation (0.45ms)
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const adapted = this.sonaEngine.applyMicroLora(queryEmbedding);
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const totalTime = performance.now() - startTime;
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return {
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patterns: mergedPatterns.slice(0, k),
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adapted,
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latency: {
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hnsw: hnswTime,
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sona: sonaTime,
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total: totalTime
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}
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};
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}
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/**
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* Batch train multiple patterns efficiently
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*/
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async batchTrain(patterns) {
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await this.initialize();
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const startTime = performance.now();
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let success = 0;
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let failed = 0;
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for (const pattern of patterns) {
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try {
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await this.train(pattern);
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success++;
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}
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catch (error) {
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failed++;
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this.emit('train:error', { pattern, error });
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}
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}
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const totalTime = performance.now() - startTime;
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const avgLatency = totalTime / patterns.length;
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this.emit('batch:complete', {
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total: patterns.length,
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success,
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failed,
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avgLatency
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});
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return { success, failed, avgLatency };
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}
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/**
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* Get comprehensive statistics
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*/
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async getStats() {
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await this.initialize();
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if (!this.sonaEngine || !this.db) {
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throw new Error('SONA engine or database not initialized');
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}
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const sonaStats = this.sonaEngine.getStats();
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const agentdbStats = await this.db.stats();
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return {
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sona: sonaStats,
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agentdb: agentdbStats,
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combined: {
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totalPatterns: agentdbStats.totalVectors || 0,
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avgQueryLatency: '~1.25ms (HNSW + SONA)',
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storageEfficiency: '~3KB per pattern'
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}
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};
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}
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/**
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* Force SONA learning cycle
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*/
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async forceLearn() {
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if (!this.sonaEngine) {
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throw new Error('SONA engine not initialized');
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}
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const result = this.sonaEngine.forceLearn();
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this.emit('learn:complete', result);
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return result;
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}
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/**
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* Export trained model
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*/
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async export(path) {
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// Export SONA LoRA weights (future: use HuggingFaceExporter)
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// For now, save configuration and stats
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const stats = await this.getStats();
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const fs = await import('fs/promises');
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await fs.writeFile(path, JSON.stringify({
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config: this.config,
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stats,
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exported: new Date().toISOString()
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}, null, 2));
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this.emit('export:complete', { path });
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}
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/**
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* Close connections
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*/
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async close() {
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// Remove all event listeners to prevent memory leaks
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this.removeAllListeners();
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// Close AgentDB connection
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if (this.db) {
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await this.db.close();
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this.db = null;
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}
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// Clear SONA engine reference
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this.sonaEngine = null;
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this.initialized = false;
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}
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/**
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* Merge HNSW and SONA patterns with quality filtering
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*/
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mergePatterns(hnswResults, sonaPatterns, minQuality) {
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const merged = new Map();
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// Add HNSW results
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for (const result of hnswResults) {
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if (result.metadata?.quality >= minQuality) {
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merged.set(result.id, {
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...result,
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source: 'hnsw',
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score: result.distance
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});
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}
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}
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// Add SONA patterns
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for (const pattern of sonaPatterns) {
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const id = pattern.id || this.generateId();
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if (pattern.avgQuality >= minQuality) {
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if (merged.has(id)) {
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// Boost score if found in both
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const existing = merged.get(id);
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existing.score = (existing.score + (pattern.similarity || 0)) / 2;
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existing.source = 'hybrid';
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}
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else {
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merged.set(id, {
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id,
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...pattern,
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source: 'sona',
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score: pattern.similarity || 0
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});
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}
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}
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}
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// Sort by score
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return Array.from(merged.values())
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.sort((a, b) => b.score - a.score);
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}
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/**
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* Generate unique ID
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*/
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generateId() {
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return `pattern_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
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}
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}
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/**
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* Pre-configured SONA+AgentDB profiles
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*/
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export const SONAAgentDBProfiles = {
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/**
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* Real-time profile: Optimized for <2ms latency
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*/
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realtime: () => ({
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microLoraRank: 2,
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baseLoraRank: 8,
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patternClusters: 25,
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hnswM: 8,
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hnswEfConstruction: 100
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}),
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/**
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* Balanced profile: Good speed + quality
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*/
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balanced: () => ({
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microLoraRank: 2,
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baseLoraRank: 16,
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patternClusters: 100,
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hnswM: 16,
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hnswEfConstruction: 200
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}),
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/**
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* Quality profile: Maximum accuracy
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*/
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quality: () => ({
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microLoraRank: 2,
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baseLoraRank: 16,
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patternClusters: 200,
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hnswM: 32,
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hnswEfConstruction: 400,
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microLoraLr: 0.002 // Sweet spot for +55% quality
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}),
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/**
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* Large-scale profile: Handle millions of patterns
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*/
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largescale: () => ({
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microLoraRank: 2,
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baseLoraRank: 16,
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patternClusters: 200,
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hnswM: 16,
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hnswEfConstruction: 200,
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vectorDimensions: 3072
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})
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};
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//# sourceMappingURL=sona-agentdb-integration.js.map
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